Data Mining Practical Machine Learning Tools and Techniques

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. 
The dataset is freely available at this https URL

SQuAD: 100,000+ Questions for Machine Comprehension of Text

Matrix Factorization Techniques for Recommender Systems

In Wireless Sensor Networks (WSNs), localization is one of the most important technologies since it plays a critical role in many applications, e.g., target tracking. If the users cannot obtain the accurate location information, the related applications cannot be accomplished. The main idea in most localization methods is that some deployed nodes (landmarks) with known coordinates (e.g., GPS-equipped nodes) transmit beacons with their coordinates in order to help other nodes localize themselves. In general, the main localization algorithms are classified into two categories: range-based and range-free. In this paper, we reclassify the localization algorithms with a new perspective based on the mobility state of landmarks and unknown nodes, and present a detailed analysis of the representative localization algorithms. Moreover, we compare the existing localization algorithms and analyze the future research directions for the localization algorithms in WSNs.

Localization algorithms of Wireless Sensor Networks: a survey

The advances in computer and wireless communication technologies have led to an increasing interest in ad hoc networks which are temporarily constructed by only mobile hosts. In ad hoc networks, since mobile hosts move freely, disconnections occur frequently, and this causes frequent network division. Consequently, data accessibility in ad hoc networks is lower than that in the conventional fixed networks. We propose three replica allocation methods to improve data accessibility by replicating data items on mobile hosts. In these three methods, we take into account the access frequency from mobile hosts to each data item and the status of the network connection. We also show the results of simulation experiments regarding the performance evaluation of our proposed methods.

/pdf/effective-replica-allocation-in-ad-hoc-networks-for-4hwlwpmx69.pdf

Effective replica allocation in ad hoc networks for improving data accessibility

For task-scheduling problems in cloud computing, a multi-objective optimization method is proposed here. First, with an aim toward the biodiversity of resources and tasks in cloud computing, we propose a resource cost model that defines the demand of tasks on resources with more details. This model reflects the relationship between the user’s resource costs and the budget costs. A multi-objective optimization scheduling method has been proposed based on this resource cost model. This method considers the makespan and the user’s budget costs as constraints of the optimization problem, achieving multi-objective optimization of both performance and cost. An improved ant colony algorithm has been proposed to solve this problem. Two constraint functions were used to evaluate and provide feedback regarding the performance and budget cost. These two constraint functions made the algorithm adjust the quality of the solution in a timely manner based on feedback in order to achieve the optimal solution. Some simulation experiments were designed to evaluate this method’s performance using four metrics: 1) the makespan; 2) cost; 3) deadline violation rate; and 4) resource utilization. Experimental results show that based on these four metrics, a multi-objective optimization method is better than other similar methods, especially as it increased 56.6% in the best case scenario.

A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing

In ad hoc networks, due to frequent network partition, data accessibility is lower than that in conventional fixed networks. In this paper, we solve this problem by replicating data items on mobile hosts. First, we propose three replica allocation methods assuming that each data item is not updated. In these three methods, we take into account the access frequency from mobile hosts to each data item and the status of the network connection. Then, we extend the proposed methods by considering aperiodic updates and integrating user profiles consisting of mobile users' schedules, access behavior, and read/write patterns. We also show the results of simulation experiments regarding the performance evaluation of our proposed methods

/pdf/data-replication-for-improving-data-accessibility-in-ad-hoc-2od73ih5nt.pdf

Data Replication for Improving Data Accessibility in Ad Hoc Networks

In ad hoc networks, since mobile hosts move freely, network division occurs frequently, and thus data accessibility is lower than that in conventional fixed networks. In this paper, assuming an environment where each data item is periodically updated, we propose three replica allocation methods to improve data accessibility by replicating data items on mobile hosts. In these three methods, we take into account the access frequency from mobile hosts to each data item, the status of the network connection, and the time remaining until each item is updated next. We also show the results of simulation experiments regarding the performance evaluation of our proposed methods. We further extend the proposed methods to adapt to an environment where aperiodic data updates occur.

Takahiro Hara

Papers

Localization algorithms of Wireless Sensor Networks: a survey

Effective replica allocation in ad hoc networks for improving data accessibility

A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing

Data Replication for Improving Data Accessibility in Ad Hoc Networks

Replica allocation methods in ad hoc networks with data update